Measuring body movement in movement disorder disease

ABSTRACT

In one example, a system for measuring body movement in a movement disorder disease is provided. The system may comprise at least one processor and a memory storing processor executable codes, which, when implemented by the at least one processor, cause the system to perform operations comprising, at least receiving a video including a sequence of images and detecting at least one object of interest in one or more of the images. Feature reference points of the at least one object of interest are located, and a virtual movement-detection framework is generated in one or more of the images. The operations may include detecting, over the sequence of images, at least one singular or reciprocating movement of the feature reference point relative to the virtual movement-detection framework and generating a virtual path tracking a path of the at least one detected singular or reciprocating movement of the feature reference point.

CLAIM OF PRIORITY

This application claims the benefit of priority to U.S. ProvisionalApplication Ser. No. 62/573,236, filed on Oct. 17, 2017, which isincorporated herein by reference in its entirety.

TECHNICAL FIELD

The subject matter disclosed herein generally relates to technicalimprovements and improved machines in measuring body movement inmovement disorder disease and, in some examples, to body movementdisorder monitoring systems and methods.

BACKGROUND

Movement disorders lack diagnostic biomarkers that identify the diseasestate and rate of progression. Instead, neurologists meet patients inperson and perform a battery of subjective tests to determine a diseasestate and recommend an appropriate treatment plan.

A treatment dilemma thus exits. Due to the location of their residenceor inability to travel, many patients with movement disorders such asParkinson's disease (PD) do not have access to a movement disordersspecialist. Those who do have access typically see their neurologist nomore than once in every six months. Standard therapy titration relies ona repeated process of clinical assessment at the office and patientinterviews. For many patients, this infrequent interaction with theirtreating physician means that they endure suboptimal treatments forextended periods.

Complex medication regimens, intraday symptom fluctuations and cognitiveissues make managing the disease a challenge for People (or Person) withParkinson's (PwP) and their caregivers. Current objective diagnosticsensors and other tools have cost and logistical barriers. They aremarketed to healthcare professionals and researchers, not PwP who areleft to manage their disease with inadequate tools.

Biopharma companies developing drugs for some diseases such as TardiveDyskinesia (TD) and Parkinson's dyskinesia are forced to rely onsuboptimal outcome measures such as the Abnormal Involuntary MovementScale (AIMS) score and The Unified Dyskinesia Rating Scale (UDysRS).Promising new medications have failed because human error, subjectivityand language and cultural issues related to these rating scales cloudclinical trial results.

The Unified Parkinson's Disease Rating Scale (UPDRS) is a scale that wasdeveloped for Parkinson's Disease (also termed PD herein) as an effortto incorporate elements from existing scales to provide a comprehensivebut efficient and flexible means to monitor PD-related disability andimpairment. The development of the UPDRS involved multiple trialversions, and the final published scale is known as MDS-UPDRS. The scaleitself has four components, largely derived from preexisting scales thatwere reviewed and modified by a consortium of movement disordersspecialists (Part I, Mentation, Behavior and Mood; Part II, Activitiesof Daily Living; Part III, Motor; Part IV, Complications). The UPDRS isfrequently utilized and for multiple purposes, including clinicalpractice. The UPDRS is an acknowledged standard in measuring diseaseprogression and to measure the clinical improvement of FDA approvedmedications in clinical trials.

Several articles have been published on scoring variability with theUPDRS. As with any scale scoring, symptoms vary from rater to rater.This variability can make it difficult to assess the impact ofimprovement of medications in clinical trials which cost millions ofdollars. Interrater reliability (IRR) has been studied and found to varyin movement disorder specialists versus other providers (generalneurologists, neurologist with other subspecialties, nurses,non-neurologists, etc.). IRR among movement disorder specialists hasbeen studied by the International Parkinson's and Movement DisorderSociety. The rates of successful certification on the motor section ofthe Unified Parkinson's Disease Rating Scale (UPDRS) after training withthe UPDRS Teaching Tape was published in 2004.

In this study only one-half of two hundred and twenty-six raters thatparticipated successfully completed certification on their firstattempt, but all completed by the third attempt. North American ratersscored better than Europeans raters. The most difficult case to rate wasthe subject with the least impairment. Standardized methods for trainingUPDRS application are essential to ensure that raters use the scaleuniformly. Raters have the greatest difficulty with the mildestimpairment, making training especially important to a study of early PD.Furthermore, at UPDRS live training sessions there are always ratersthat have a 1- to 3-point difference in scoring even when rating thesame patient video.

The present disclosure seeks to address these significant technical andmedical drawbacks by providing improved technology, as described furtherbelow, aimed at solving these problems.

SUMMARY

In some embodiments, there is provided a system for measuring bodymovement in movement disorder disease, the system comprising a computingdevice including at least one processor and a memory storing processorexecutable codes, which, when implemented by the at least one processor,cause the system to perform the steps of: receiving a video including asequence of images; detecting at least one object of interest in one ormore of the images; locating feature reference points of the at leastone object of interest; generating a virtual movement-detectionframework in one or more of the images; aligning the virtualmovement-detection framework with the at least one object of interest inone or more of the images based at least in part on a feature referencepoint; detecting, in real-time, over the sequence of images, at leastone singular or reciprocating movement of the feature reference pointrelative to the virtual movement-detection framework; generating avirtual path tracking a path of the at least one detected movement ofthe feature reference point; analyzing at least coordinates of thevirtual path or feature reference point and associating the detected atleast one movement with a body movement disorder selected from aplurality of body movement disorders; generating or presenting adisorder status of an individual based on the associated body movementdisorder selected from the plurality of body movement disorders; andgenerating a communication including data associated with the disorderstatus based on or including a trend in the disorder status.

Some embodiments of the present inventive subject matter include methodsfor measuring body movement in movement disorder disease. In oneexample, a method comprises: receiving a video including a sequence ofimages; detecting at least one object of interest in one or more of theimages; locating feature reference points of the at least one object ofinterest; generating a virtual movement-detection framework in one ormore of the images; aligning the virtual movement-detection frameworkwith the at least one object of interest in one or more of the imagesbased at least in part on a feature reference point; detecting, inreal-time, over the sequence of images, at least one singular orreciprocating movement of the feature reference point relative to thevirtual movement-detection framework; generating a virtual path trackinga path of the at least one detected movement of the feature referencepoint; analyzing at least coordinates of the virtual path or featurereference point and associating the detected at least one movement witha body movement disorder selected from a plurality of body movementdisorders; generating or presenting a disorder status of an individualbased on the associated body movement disorder selected from theplurality of body movement disorders; and generating a communicationincluding data associated with the disorder status based on or includinga trend in the disorder status.

Some embodiments may include machine-readable media includinginstructions which, when read by a machine, cause the machine to performthe operations of any one or more of the methodologies described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Some embodiments are illustrated by way of example and not limitation inthe figures of the accompanying drawings that appear below.

FIG. 1 is a block diagram illustrating a networked system, according toan example embodiment.

FIG. 2 is a block diagram showing for the architectural details of apublication system, according to some example embodiments.

FIG. 3 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 4 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform operations of any one or more of the methodologies discussedherein.

FIGS. 5A-5B depict schematic views of aspects of facial recognition inan Arising-From-Chair test, according to example embodiments.

FIGS. 6A-6B depict schematic views of aspects of finger tracking in aFinger Tapping test, according to example embodiments.

FIG. 7 is a schematic view of detected facial landmarks and ameasurement grid displayed at face depth, according to an exampleembodiment.

FIG. 8 is a schematic view of a location and proportion of faciallandmarks, according to an example embodiment.

FIG. 9 is a schematic view of a facial landmark movement amplitude overtime validated with video frame grabs, according to an exampleembodiment.

FIG. 10 is a schematic view of a facial landmark image amplitude takenfrom a movement analysis displayed with a grid and without the subject'sfacial image, according to an example embodiment.

FIG. 11 is a schematic view of subject facial landmarks with eyes open,according to an example embodiment.

FIG. 12 is a schematic view of subject facial landmarks with eyesclosed, according to an example embodiment.

FIG. 13 is a schematic view of subject facial landmarks with headtilted, according to an example embodiment.

FIG. 14 is a schematic view of subject facial landmark amplitude overtime, according to an example embodiment.

FIG. 15 is a schematic view of body part amplitude, according to anexample embodiment.

FIG. 16 is a schematic view of body part amplitude in a privacy mode(subject images removed), according to an example embodiment.

FIGS. 17A-17D represent certain observations, operations and graphedresults in an example image processing of a video to measure andvisualize a level of a subject's dyskinesia, according to exampleembodiments.

FIG. 18 depicts an example face pose output format, according to anexample embodiment.

FIG. 19 depicts an example body pose output format, according to anexample embodiment.

FIGS. 20A-20D represent different poses of a subject alongside extractedskeletons displayed within a measurement or reference grid overlaidimages in a video and sized according to a reference scale, according toexample embodiments.

FIGS. 21A-21D depict example movement trajectories of an assessedsubject, according to example embodiments.

FIGS. 22A-22D depict example graphs with results plotted for x and ydisplacement of an example neck key point illustrating various levels ofdyskinesia, according to example embodiments.

FIGS. 23A-23D depict example graphs with results representing measuredfrequencies corresponding to different levels of dyskinesia, accordingto example embodiments.

FIGS. 24-25 depict example operations in methods, according to exampleembodiments.

FIGS. 26-27 depict example architectures, according to exampleembodiments.

FIGS. 28, 29, 30A, 30B and 31 illustrate further aspects of bodymovement measurement, according to example embodiments.

FIG. 32 is a flowchart of operations in a method, according to anexample embodiment.

DETAILED DESCRIPTION

“Carrier Signal” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine, and includes digital or analog communications signals orother intangible medium to facilitate communication of suchinstructions. Instructions may be transmitted or received over thenetwork using a transmission medium via a network interface device andusing any one of a number of well-known transfer protocols.

“Client Device” in this context refers to any machine that interfaces toa communications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smart phones, tablets, ultra-books, netbooks,laptops, multi-processor systems, microprocessor-based or programmableconsumer electronics, game consoles, set-top boxes, or any othercommunication device that a user may use to access a network.

“Communications Network” in this context refers to one or more portionsof a network that may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network may include a wireless or cellular network andthe coupling may be a Code Division Multiple Access (CDMA) connection, aGlobal System for Mobile communications (GSM) connection, or other typeof cellular or wireless coupling. In this example, the coupling mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long Term Evolution (LTE)standard, others defined by various standard setting organizations,other long range protocols, or other data transfer technology.

“Component” in this context refers to a device, physical entity or logichaving boundaries defined by function or subroutine calls, branchpoints, application program interfaces (APIs), or other technologiesthat provide for the partitioning or modularization of particularprocessing or control functions. Components may be combined via theirinterfaces with other components to carry out a machine process. Acomponent may be a packaged functional hardware unit designed for usewith other components and a part of a program that usually performs aparticular function of related functions. Components may constituteeither software components (e.g., code embodied on a machine-readablemedium) or hardware components.

A “hardware component” is a tangible unit capable of performing certainoperations and may be configured or arranged in a certain physicalmanner. In various example embodiments, one or more computer systems(e.g., a standalone computer system, a client computer system, or aserver computer system) or one or more hardware components of a computersystem (e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwarecomponent that operates to perform certain operations as describedherein. A hardware component may also be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware component may include dedicated circuitry or logic that ispermanently configured to perform certain operations. A hardwarecomponent may be a special-purpose processor, such as aField-Programmable Gate Array (FPGA) or an Application SpecificIntegrated Circuit (ASIC). A hardware component may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors.

It will be appreciated that the decision to implement a hardwarecomponent mechanically, in dedicated and permanently configuredcircuitry, or in temporarily configured circuitry (e.g., configured bysoftware) may be driven by cost and time considerations. Accordingly,the phrase “hardware component” (or “hardware-implemented component”)should be understood to encompass a tangible entity, be that an entitythat is physically constructed, permanently configured (e.g.,hardwired), or temporarily configured (e.g., programmed) to operate in acertain manner or to perform certain operations described herein.Considering embodiments in which hardware components are temporarilyconfigured (e.g., programmed), each of the hardware components need notbe configured or instantiated at any one instance in time. For example,where a hardware component comprises a general-purpose processorconfigured by software to become a special-purpose processor, thegeneral-purpose processor may be configured as respectively differentspecial-purpose processors (e.g., comprising different hardwarecomponents) at different times. Software accordingly configures aparticular processor or processors, for example, to constitute aparticular hardware component at one instance of time and to constitutea different hardware component at a different instance of time. Hardwarecomponents can provide information to, and receive information from,other hardware components. Accordingly, the described hardwarecomponents may be regarded as being communicatively coupled. Wheremultiple hardware components exist contemporaneously, communications maybe achieved through signal transmission (e.g., over appropriate circuitsand buses) between or among two or more of the hardware components. Inembodiments in which multiple hardware components are configured orinstantiated at different times, communications between such hardwarecomponents may be achieved, for example, through the storage andretrieval of information in memory structures to which the multiplehardware components have access. For example, one hardware component mayperform an operation and store the output of that operation in a memorydevice to which it is communicatively coupled. A further hardwarecomponent may then, at a later time, access the memory device toretrieve and process the stored output. Hardware components may alsoinitiate communications with input or output devices, and can operate ona resource (e.g., a collection of information).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an Application ProgramInterface (API)). The performance of certain of the operations may bedistributed among the processors, not only residing within a singlemachine, but deployed across a number of machines. In some exampleembodiments, the processors or processor-implemented components may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“Machine-Readable Medium” in this context refers to a component, deviceor other tangible media able to store instructions and data temporarilyor permanently and may include, but not be limited to, random-accessmemory (RAM), read-only memory (ROM), buffer memory, flash memory,optical media, magnetic media, cache memory, other types of storage(e.g., Erasable Programmable Read-Only Memory (EEPROM)) and/or anysuitable combination thereof. The term “machine-readable medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions. The term “machine-readable medium” shallalso be taken to include any medium, or combination of multiple media,that is capable of storing instructions (e.g., code) for execution by amachine, such that the instructions, when executed by one or moreprocessors of the machine, cause the machine to perform any one or moreof the methodologies described herein. Accordingly, a “machine-readablemedium” refers to a single storage apparatus or device, as well as“cloud-based” storage systems or storage networks that include multiplestorage apparatus or devices. The term “machine-readable medium”excludes signals per se.

“Processor” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor)that manipulates data values according to control signals (e.g.,“commands”, “op codes”, “machine code”, etc.) and which producescorresponding output signals that are applied to operate a machine. Aprocessor may, for example, be a Central Processing Unit (CPU), aReduced Instruction Set Computing (RISC) processor, a ComplexInstruction Set Computing (CISC) processor, a Graphics Processing Unit(GPU), a Digital Signal Processor (DSP), an Application SpecificIntegrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC)or any combination thereof. A processor may further be a multi-coreprocessor having two or more independent processors (sometimes referredto as “cores”) that may execute instructions contemporaneously.

A portion of the disclosure of this patent document contains materialthat is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent files or records, but otherwise reserves all copyrightrights whatsoever. The following notice applies to the software and dataas described below and in the drawings that form a part of thisdocument: Copyright 2017-2018, Beneufit, Inc., All Rights Reserved.

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

With reference to FIG. 1, an example embodiment of a high-level SaaSnetwork architecture 100 is shown. A networked system 116 providesserver-side functionality via a network 110 (e.g., the Internet or widearea network (WAN)) to a client device 108. A web client 102 and aprogrammatic client, in the example form of an application 104, arehosted and execute on the client device 108. The networked system 116includes an application server 122, which in turn hosts a publicationsystem 106 (e.g. a publication platform hosted athttps://dash.beneufit.com/) that provides a number of functions andservices to the application 104 that accesses the networked system 116.The application 104 also provides a number of interfaces describedherein, which present output of the scheduling operations to a user ofthe client device 108.

The client device 108 enables a user to access and interact with thenetworked system 116, and ultimately the publication system 106. Forinstance, the user provides input (e.g., touch screen input oralphanumeric input) to the client device 108, and the input iscommunicated to the networked system 116 via the network 110. In thisinstance, the networked system 116, in response to receiving the inputfrom the user, communicates information back to the client device 108via the network 110 to be presented to the user.

An Application Program Interface (API) server 118 and a web server 120are coupled, and provide programmatic and web interfaces respectively,to the application server 122. The application server 122 hosts thepublication system 106, which includes components or applicationsdescribed further below. The application server 122 is, in turn, shownto be coupled to a database server 124 that facilitates access toinformation storage repositories (e.g., a database 126). In an exampleembodiment, the database 126 includes storage devices that storeinformation accessed and generated by the publication system 106. Thedatabase 126 may include a real-time database as described elsewhereherein.

Additionally, a third-party application 114, executing on a third-partyserver(s) 112, is shown as having programmatic access to the networkedsystem 116 via the programmatic interface provided by the ApplicationProgram Interface (API) server 118. For example, the third-partyapplication 114, using information retrieved from the networked system116, may support one or more features or functions on a website hostedby the third party.

Turning now specifically to the applications hosted by the client device108, the web client 102 may access the various systems (e.g.,publication system 106) via the web interface supported by the webserver 120. Similarly, the application 104 (e.g., an “app” such asPDFit) accesses the various services and functions provided by thepublication system 106 via the programmatic interface provided by theApplication Program Interface (API) server 118. The application 104 maybe, for example, an “app” executing on a client device 108, such as aniOS or Android OS application to enable a user to access and input dataon the networked system 116 in an off-line manner, and to performbatch-mode communications between the programmatic client application104 and the networked system networked system 116.

Further, while the SaaS network architecture 100 shown in FIG. 1 employsa client-server architecture, the present inventive subject matter isnot limited to such an architecture, and could equally well findapplication in a distributed, or peer-to-peer, architecture system, forexample. The publication system 106 could also be implemented as astandalone software program, which does not necessarily have networkingcapabilities.

FIG. 2 is a block diagram showing architectural details of a publicationsystem 106, according to some example embodiments. Specifically, thepublication system 106 is shown to include an interface component 210 bywhich the publication system 106 communicates (e.g., over the network208) with other systems within the SaaS network architecture 100.

The interface component 210 is communicatively coupled to an interactiveworkflow component 206 that operates, in conjunction with a real-timedatabase 126, to provide multiscreen interactive workflow facilitationservices in accordance with the methods described further below withreference to the accompanying drawings.

FIG. 3 is a block diagram illustrating an example software architecture306, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 3 is a non-limiting example of asoftware architecture 306 and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 306 may execute on hardwaresuch as machine 400 of FIG. 4 that includes, among other things,processors 404, memory/storage 406, and I/O components 418. Arepresentative hardware layer 352 is illustrated and can represent, forexample, the machine 400 of FIG. 4. The representative hardware layer352 includes a processing unit 354 having associated executableinstructions 304. Executable instructions 304 represent the executableinstructions of the software architecture 306, including implementationof the methods, components and so forth described herein. The hardwarelayer 352 also includes memory and/or storage modules as memory/storage356, which also have executable instructions 304. The hardware layer 352may also comprise other hardware 358.

In the example architecture of FIG. 3, the software architecture 306 maybe conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 306 mayinclude layers such as an operating system 302, libraries 320,applications 316 and a presentation layer 314. Operationally, theapplications 316 and/or other components within the layers may invokeapplication programming interface (API) API calls 308 through thesoftware stack and receive a response as messages 312 in response to theAPI calls 308. The layers illustrated are representative in nature andnot all software architectures have all layers. For example, some mobileor special purpose operating systems may not provide aframeworks/middleware 318, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 302 may manage hardware resources and providecommon services. The operating system 302 may include, for example, akernel 322, services 324 and drivers 326. The kernel 322 may act as anabstraction layer between the hardware and the other software layers.For example, the kernel 322 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 324 may provideother common services for the other software layers. The drivers 326 areresponsible for controlling or interfacing with the underlying hardware.For instance, the drivers 326 include display drivers, camera drivers,Bluetooth® drivers, flash memory drivers, serial communication drivers(e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audiodrivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 320 provide a common infrastructure that is used by theapplications 316 and/or other components and/or layers. The libraries320 provide functionality that allows other software components toperform tasks in an easier fashion than to interface directly with theunderlying operating system 302 functionality (e.g., kernel 322,services 324 and/or drivers 326). The libraries 320 may include systemlibraries 344 (e.g., C standard library) that may provide functions suchas memory allocation functions, string manipulation functions,mathematical functions, and the like. In addition, the libraries 320 mayinclude API libraries 346 such as media libraries (e.g., libraries tosupport presentation and manipulation of various media format such asMPREG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., anOpenGL framework that may be used to render 2D and 3D in a graphiccontent on a display), database libraries (e.g., SQLite that may providevarious relational database functions), web libraries (e.g., WebKit thatmay provide web browsing functionality), and the like. The libraries 320may also include a wide variety of other libraries 348 to provide manyother APIs to the applications 316 and other softwarecomponents/modules.

The frameworks/middleware 318 (also sometimes referred to as middleware)provide a higher-level common infrastructure that may be used by theapplications 316 and/or other software components/modules. For example,the frameworks/middleware 318 may provide various graphic user interface(GUI) functions, high-level resource management, high-level locationservices, and so forth. The frameworks/middleware 318 may provide abroad spectrum of other APIs that may be utilized by the applications316 and/or other software components/modules, some of which may bespecific to a particular operating system or platform.

The applications 316 include built-in applications 338 and/orthird-party applications 340. Examples of representative built-inapplications 338 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. Third-party applications 340 may include anyapplication developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platformand may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 340 may invoke the API calls 308 provided bythe mobile operating system (such as operating system 302) to facilitatefunctionality described herein.

The applications 316 may use built-in operating system functions (e.g.,kernel 322, services 324 and/or drivers 326), libraries 320, andframeworks/middleware 318 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systems,interactions with a user may occur through a presentation layer, such aspresentation layer 314. In these systems, the application/component“logic” can be separated from the aspects of the application/componentthat interact with a user.

Some software architectures use virtual machines. In the example of FIG.3, this is illustrated by a virtual machine 310. The virtual machine 310creates a software environment where applications/components can executeas if they were executing on a hardware machine (such as the machine 400of FIG. 4, for example). The virtual machine 310 is hosted by a hostoperating system (operating system (OS) 336 in FIG. 3) and typically,although not always, has a virtual machine monitor 360, which managesthe operation of the virtual machine 310 as well as the interface withthe host operating system (i.e., operating system 302). A softwarearchitecture executes within the virtual machine 310 such as anoperating system (OS) 336, libraries 334, frameworks 332, applications330 and/or presentation layer 328. These layers of software architectureexecuting within the virtual machine 310 can be the same ascorresponding layers previously described or may be different.

FIG. 4 is a block diagram illustrating components of a machine 400,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 4 shows a diagrammatic representation of the machine400 in the example form of a computer system, within which instructions410 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 400 to perform any one ormore of the methodologies discussed herein may be executed. As such, theinstructions 410 may be used to implement modules or componentsdescribed herein. The instructions 410 transform the general,non-programmed machine into a particular machine programmed to carry outthe described and illustrated functions in the manner described. Inalternative embodiments, the machine 400 operates as a standalone deviceor may be coupled (e.g., networked) to other machines. In a networkeddeployment, the machine 400 may operate in the capacity of a servermachine or a client machine in a server-client network environment, oras a peer machine in a peer-to-peer (or distributed) networkenvironment. The machine 400 may comprise, but not be limited to, aserver computer, a client computer, a personal computer (PC), a tabletcomputer, a laptop computer, a netbook, a set-top box (STB), a personaldigital assistant (PDA), an entertainment media system, a cellulartelephone, a smart phone, a mobile device, a wearable device (e.g., asmart watch), a smart home device (e.g., a smart appliance), other smartdevices, a web appliance, a network router, a network switch, a networkbridge, or any machine capable of executing the instructions 410,sequentially or otherwise, that specify actions to be taken by machine400. Further, while only a single machine 400 is illustrated, the term“machine” shall also be taken to include a collection of machines thatindividually or jointly execute the instructions 410 to perform any oneor more of the methodologies discussed herein.

The machine 400 may include processors 404, memory/storage 406, and I/Ocomponents 418, which may be configured to communicate with each othersuch as via a bus 402. The memory/storage 406 may include a memory 414,such as a main memory, or other memory storage, and a storage unit 416,both accessible to the processors 404 such as via the bus 402. Thestorage unit 416 and memory 414 store the instructions 410 embodying anyone or more of the methodologies or functions described herein. Theinstructions 410 may also reside, completely or partially, within thememory 414, within the storage unit 416, within at least one of theprocessors 404 (e.g., within the processor's cache memory), or anysuitable combination thereof, during execution thereof by the machine400. Accordingly, the memory 414, the storage unit 416, and the memoryof processors 404 are examples of machine-readable media.

The I/O components 418 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 418 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones will likely include a touch input device or other such inputmechanisms, while a headless server machine will likely not include sucha touch input device. It will be appreciated that the I/O components 418may include many other components that are not shown in FIG. 4. The I/Ocomponents 418 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 418 mayinclude output components 426 and input components 428. The outputcomponents 426 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 428 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 418 may includebiometric components 430, motion components 434, environment components436, or position components 438 among a wide array of other components.For example, the biometric components 430 may include components todetect expressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye tracking), measure bio signals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram based identification), and the like. The motioncomponents 434 may include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope), and so forth. The environment components436 may include, for example, illumination sensor components (e.g.,photometer), temperature sensor components (e.g., one or morethermometer that detect ambient temperature), humidity sensorcomponents, pressure sensor components (e.g., barometer), acousticsensor components (e.g., one or more microphones that detect backgroundnoise), proximity sensor components (e.g., infrared sensors that detectnearby objects), gas sensors (e.g., gas detection sensors to detectionconcentrations of hazardous gases for safety or to measure pollutants inthe atmosphere), or other components that may provide indications,measurements, or signals corresponding to a surrounding physicalenvironment. The position components 438 may include location sensorcomponents (e.g., a Global Position System (GPS) receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 418 may include communication components 440 operableto couple the machine 400 to a network 432 or devices 420 via coupling422 and coupling 424 respectively. For example, the communicationcomponents 440 may include a network interface component or othersuitable device to interface with the network 432. In further examples,communication components 440 may include wired communication components,wireless communication components, cellular communication components,Near Field Communication (NFC) components, Bluetooth® components (e.g.,Bluetooth® Low Energy), Wi-Fi® components, and other communicationcomponents to provide communication via other modalities. The devices420 may be another machine or any of a wide variety of peripheraldevices (e.g., a peripheral device coupled via a Universal Serial Bus(USB)).

Moreover, the communication components 440 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 440 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components440, such as location via Internet Protocol (IP) geo-location, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

As mentioned above, many PwP, due to the location of their residence orinability to travel, do not have access to a movement disordersspecialist. Those who do have access typically see their neurologist nomore than every six months. Standard therapy titration relies on arepeated process of clinical assessment at the office and patientinterviews. For many patients, this infrequent interaction with theirtreating physician means that they endure suboptimal treatments forextended periods. For example, the battery of subjective tests referredto in the Background above include the following tests. For clarity ofunderstanding, the description of these tests is based on the Part 3Motor Test section of the UPDRS. Any copyright in this material sourcedfrom the Movement Disorder Society is acknowledged. It will be apparentthat the tests rely on a high degree of examiner subjectivity and areprone to human error.

3.1 SPEECH Instructions to examiner: Listen to the patient'sfree-flowing speech and engage in conversation if necessary. Suggestedtopics: ask about the patient's work, hobbies, exercise, or how s/he gotto the doctor's office. Evaluate volume, modulation (prosody) andclarity, including slurring, palilalia (repetition of syllables) andtachyphemia (rapid speech, running syllables together).

0: Normal: No speech problems. 1: Slight: Loss of modulation, diction orvolume, but still all words easy to understand. 2: Mild: Loss ofmodulation, diction, or volume, with a few words unclear, but theoverall sentences easy to follow. 3: Moderate: Speech is difficult tounderstand to the point that some, but not most, sentences are poorlyunderstood. 4: Severe: Most speech is difficult to understand orunintelligible.

3.2 FACIAL EXPRESSION Instructions to examiner: Observe the patientsitting at rest for 10 seconds, without talking and also while talking.Observe eye-blink frequency, masked facies or loss of facial expression,spontaneous smiling and parting of lips.

0: Normal: Normal facial expression. 1: Slight: Minimal masked faciesmanifested only by decreased frequency of blinking. 2: Mild: In additionto decreased eye-blink frequency, Masked facies present in the lowerface as well, namely fewer movements around the mouth, such as lessspontaneous smiling, but lips not parted. 3: Moderate: Masked facieswith lips parted some of the time when the mouth is at rest. 4: Severe:Masked facies with lips parted most of the time when the mouth is atrest.

3.3 RIGIDITY Instructions to examiner: Rigidity is judged on slowpassive movement of major joints with the patient in a relaxed positionand the examiner manipulating the limbs and neck. First, test without anactivation maneuver. Test and rate neck and each limb separately. Forarms, test the wrist and elbow joints simultaneously. For legs, test thehip and knee joints simultaneously. If no rigidity is detected, use anactivation maneuver such as tapping fingers, fist opening/closing, orheel tapping in a limb not being tested. Explain to the patient to go aslimp as possible as you test for rigidity.

0: Normal: No rigidity. 1: Slight: Rigidity only detected withactivation maneuver. 2: Mild: Rigidity detected without the activationmaneuver, but full range of motion is easily achieved. 3: Moderate:Rigidity detected without the activation maneuver; full range of motionis achieved with effort. 4: Severe: Rigidity detected without theactivation maneuver and full range of motion not achieved.

3.4 FINGER TAPPING Instructions to examiner: Each hand is testedseparately. Demonstrate the task, but do not continue to perform thetask while the patient is being tested. Instruct the patient to tap theindex finger on the thumb 10 times as quickly AND as big as possible.Rate each side separately, evaluating speed, amplitude, hesitations,halts and decrementing amplitude.

0: Normal: No problems. 1: Slight: Any of the following: a) the regularrhythm is broken with one or two interruptions or hesitations of thetapping movement; b) slight slowing; c) the amplitude decrements nearthe end of the 10 taps. 2: Mild: Any of the following: a) 3 to 5interruptions during tapping; b) mild slowing; c) the amplitudedecrements midway in the 10-tap sequence. 3: Moderate: Any of thefollowing: a) more than 5 interruptions during tapping or at least onelonger arrest (freeze) in ongoing movement; b) moderate slowing; c) theamplitude decrements starting after the 1st tap. 4: Severe: Cannot orcan only barely perform the task because of slowing, interruptions ordecrements.

3.5 HAND MOVEMENTS Instructions to examiner: Test each hand separately.Demonstrate the task, but do not continue to perform the task while thepatient is being tested. Instruct the patient to make a tight fist withthe arm bent at the elbow so that the palm faces the examiner. Have thepatient open the hand 10 times as fully AND as quickly as possible. Ifthe patient fails to make a tight fist or to open the hand fully, remindhim/her to do so. Rate each side separately, evaluating speed,amplitude, hesitations, halts and decrementing amplitude.

0: Normal: No problem. 1: Slight: Any of the following: a) the regularrhythm is broken with one or two interruptions or hesitations of themovement; b) slight slowing; c) the amplitude decrements near the end ofthe task. 2: Mild: Any of the following: a) 3 to 5 interruptions duringthe movements; b) mild slowing; c) the amplitude decrements midway inthe task. 3: Moderate: Any of the following: a) more than 5interruptions during the movement or at least one longer arrest (freeze)in ongoing movement; b) moderate slowing; c) the amplitude decrementsstarting after the 1st open-and-close sequence. 4: Severe: Cannot or canonly barely perform the task because of slowing, interruptions ordecrements.

3.6 PRONATION-SUPINATION MOVEMENTS OF HANDS Instructions to examiner:Test each hand separately. Demonstrate the task, but do not continue toperform the task while the patient is being tested. Instruct the patientto extend the arm out in front of his/her body with the palms down; thento turn the palm up and down alternately 10 times as fast and as fullyas possible. Rate each side separately, evaluating speed, amplitude,hesitations, halts and decrementing amplitude.

0: Normal: No problems. 1: Slight: Any of the following: a) the regularrhythm is broken with one or two interruptions or hesitations of themovement; b) slight slowing; c) the amplitude decrements near the end ofthe sequence. 2: Mild: Any of the following: a) 3 to 5 interruptionsduring the movements; b) mild slowing; c) the amplitude decrementsmidway in the sequence. 3: Moderate: Any of the following: a) more than5 interruptions during the movement or at least one longer arrest(freeze) in ongoing movement; b) moderate slowing c) the amplitudedecrements starting after the 1st supination-pronation sequence. 4:Severe: Cannot or can only barely perform the task because of slowing,interruptions or decrements.

3.7 TOE TAPPING Instructions to examiner: Have the patient sit in astraight-backed chair with arms, both feet on the floor. Test each footseparately. Demonstrate the task, but do not continue to perform thetask while the patient is being tested. Instruct the patient to placethe heel on the ground in a comfortable position and then tap the toes10 times as big and as fast as possible. Rate each side separately,evaluating speed, amplitude, hesitations, halts and decrementingamplitude.

0: Normal: No problem. 1: Slight: Any of the following: a) the regularrhythm is broken with one or two interruptions or hesitations of thetapping movement; b) slight slowing; c) amplitude decrements near theend of the ten taps. 2: Mild: Any of the following: a) 3 to 5interruptions during the tapping movements; b) mild slowing; c)amplitude decrements midway in the task. 3: Moderate: Any of thefollowing: a) more than 5 interruptions during the tapping movements orat least one longer arrest (freeze) in ongoing movement; b) moderateslowing; c) amplitude decrements after the first tap. 4: Severe: Cannotor can only barely perform the task because of slowing, interruptions ordecrements.

3.8 LEG AGILITY Instructions to examiner: Have the patient sit in astraight-backed chair with arms. The patient should have both feetcomfortably on the floor. Test each leg separately. Demonstrate thetask, but do not continue to perform the task while the patient is beingtested. Instruct the patient to place the foot on the ground in acomfortable position and then raise and stomp the foot on the ground 10times as high and as fast as possible. Rate each side separately,evaluating speed, amplitude, hesitations, halts and decrementingamplitude.

0: Normal: No problems. 1: Slight: Any of the following: a) the regularrhythm is broken with one or two interruptions or hesitations of themovement; b) slight slowing; c) amplitude decrements near the end of thetask. 2: Mild: Any of the following: a) 3 to 5 interruptions during themovements; b) mild slowness; c) amplitude decrements midway in the task.3: Moderate: Any of the following: a) more than 5 interruptions duringthe movement or at least one longer arrest (freeze) in ongoing movement;b) moderate slowing in speed; c) amplitude decrements after the firsttap. 4: Severe: Cannot or can only barely perform the task because ofslowing, interruptions or decrements.

3.9 ARISING FROM CHAIR Instructions to examiner: Have the patient sit ina straight-backed chair with arms, with both feet on the floor andsitting back in the chair (if the patient is not too short). Ask thepatient to cross his/her arms across the chest and then to stand up. Ifthe patient is not successful, repeat this attempt a maximum up to twomore times. If still unsuccessful, allow the patient to move forward inthe chair to arise with arms folded across the chest. Allow only oneattempt in this situation. If unsuccessful, allow the patient to pushoff using his/her hands on the arms of the chair. Allow a maximum ofthree trials of pushing off. If still not successful, assist the patientto arise. After the patient stands up, observe the posture for item3.13.

0: Normal: No problems. Able to arise quickly without hesitation. 1:Slight: Arising is slower than normal; or may need more than oneattempt; or may need to move forward in the chair to arise. No need touse the arms of the chair. 2: Mild: Pushes self up from arms of chairwithout difficulty. 3: Moderate: Needs to push off but tends to fallback; or may have to try more than one time using arms of chair but canget up without help. 4: Severe: Unable to arise without help.

3.10 GAIT Instructions to examiner: Testing gait is best performed byhaving the patient walking away from and towards the examiner so thatboth right and left sides of the body can be easily observedsimultaneously. The patient should walk at least 10 meters (30 feet),then turn around and return to the examiner. This item measures multiplebehaviors: stride amplitude, stride speed, height of foot lift, heelstrike during walking, turning, and arm swing, but not freezing. Assessalso for “freezing of gait” (next item 3.11) while patient is walking.Observe posture for item 3.13.

0: Normal: No problems. 1: Slight: Independent walking with minor gaitimpairment. 2: Mild: Independent walking but with substantial gaitimpairment. 3: Moderate: Requires an assistance device for safe walking(walking stick, walker) but not a person. 4: Severe: Cannot walk at allor only with another person's assistance.

3.11 FREEZING OF GAIT Instructions to examiner: While assessing gait,also assess for the presence of any gait freezing episodes. Observe forstart hesitation and stuttering movements especially when turning andreaching the end of the task. To the extent that safety permits,patients may NOT use sensory tricks during the assessment.

0: Normal: No freezing. 1: Slight: Freezes on starting, turning orwalking through doorway with a single halt during any of these events,but then continues smoothly without freezing during straight walking. 2:Mild: Freezes on starting, turning or walking through doorway with morethan one halt during any of these activities, but continues smoothlywithout freezing during straight walking. 3: Moderate: Freezes onceduring straight walking. 4: Severe: Freezes multiple times duringstraight walking.

3.12 POSTURAL STABILITY Instructions to examiner: The test examines theresponse to sudden body displacement produced by a quick, forceful pullon the shoulders while the patient is standing erect with eyes open andfeet comfortably apart and parallel to each other. Test retropulsion.Stand behind the patient and instruct the patient on what is about tohappen. Explain that s/he is allowed to take a step backwards to avoidfalling. There should be a solid wall behind the examiner, at least 1-2meters away to allow for the observation of the number of retropulsivesteps. The first pull is an instructional demonstration and is purposelymilder and not rated. The second time the shoulders are pulled brisklyand forcefully towards the examiner with enough force to displace thecenter of gravity so that patient MUST take a step backwards. Theexaminer needs to be ready to catch the patient but must standsufficiently back so as to allow enough room for the patient to takeseveral steps to recover independently. Do not allow the patient to flexthe body abnormally forward in anticipation of the pull. Observe for thenumber of steps backwards or falling. Up to and including two steps forrecovery is considered normal, so abnormal ratings begin with threesteps. If the patient fails to understand the test, the examiner canrepeat the test so that the rating is based on an assessment that theexaminer feels reflects the patient's limitations rather thanmisunderstanding or lack of preparedness. Observe standing posture foritem 3.13.

0: Normal: No problems: Recovers with one or two steps. 1: Slight: 3-5steps, but subject recovers unaided. 2: Mild: More than 5 steps, butsubject recovers unaided. 3: Moderate: Stands safely, but with absenceof postural response; falls if not caught by examiner. 4: Severe: Veryunstable, tends to lose balance spontaneously or with just a gentle pullon the shoulders.

3.13 POSTURE Instructions to examiner: Posture is assessed with thepatient standing erect after arising from a chair, during walking, andwhile being tested for postural reflexes. If you notice poor posture,tell the patient to stand up straight and see if the posture improves(see option 2 below). Rate the worst posture seen in these threeobservation points. Observe for flexion and side-to-side leaning.

0: Normal: No problems. 1: Slight: Not quite erect, but posture could benormal for older person. 2: Mild: Definite flexion, scoliosis or leaningto one side, but patient can correct posture to normal posture whenasked to do so. 3: Moderate: Stooped posture, scoliosis or leaning toone side that cannot be corrected volitionally to a normal posture bythe patient 4: Severe: Very unstable, tends to lose balancespontaneously or with just a gentle pull on the shoulders.

3.14 GLOBAL SPONTANEITY OF MOVEMENT (BODY BRADYKINESIA) Instructions toexaminer: This global rating combines all observations on slowness,hesitancy, and small amplitude and poverty of movement in general,including a reduction of gesturing and of crossing the legs. Thisassessment is based on the examiner's global impression after observingfor spontaneous gestures while sitting, and the nature of arising andwalking.

0: Normal: No problems. 1: Slight: Slight global slowness and poverty ofspontaneous movements. 2: Mild: Mild global slowness and poverty ofspontaneous movements. 3: Moderate: Moderate global slowness and povertyof spontaneous movements. 4: Severe: Severe global slowness and povertyof spontaneous movements.

3.15 POSTURAL TREMOR OF THE HANDS Instructions to examiner: All tremor,including re-emergent rest tremor, that is present in this posture is tobe included in this rating. Rate each hand separately. Rate the highestamplitude seen. Instruct the patient to stretch the arms out in front ofthe body with palms down. The wrist should be straight, and the fingerscomfortably separated so that they do not touch each other. Observe thisposture for 10 seconds.

0: Normal: No tremor. 1: Slight: Tremor is present but less than 1 cm inamplitude. 2: Mild: Tremor is at least 1 but less than 3 cm inamplitude. 3: Moderate: Tremor is at least 3 but less than 10 cm inamplitude. 4: Severe: Tremor is at least 10 cm in amplitude.

3.16 KINETIC TREMOR OF THE HANDS Instructions to examiner: This istested by the finger-to-nose maneuver. With the arm starting from theoutstretched position, have the patient perform at least threefinger-to-nose maneuvers with each hand reaching as far as possible totouch the examiner's finger. The finger-to-nose maneuver should beperformed slowly enough not to hide any tremor that could occur withvery fast arm movements. Repeat with the other hand, rating each handseparately. The tremor can be present throughout the movement or as thetremor reaches either target (nose or finger). Rate the highestamplitude seen.

0: Normal: No tremor. 1: Slight: Tremor is present but less than 1 cm inamplitude. 2: Mild: Tremor is at least 1 but less than 3 cm inamplitude. 3: Moderate: Tremor is at least 3 but less than 10 cm inamplitude. 4: Severe: Tremor is at least 10 cm in amplitude.

3.17 REST TREMOR AMPLITUDE Instructions to examiner: This and the nextitem have been placed purposefully at the end of the examination toallow the rater to gather observations on rest tremor that may appear atany time during the exam, including when quietly sitting, during walkingand during activities when some body parts are moving but others are atrest. Score the maximum amplitude that is seen at any time as the finalscore. Rate only the amplitude and not the persistence or theintermittency of the tremor.

As part of this rating, the patient should sit quietly in a chair withthe hands placed on the arms of the chair (not in the lap) and the feetcomfortably supported on the floor for 10 seconds with no otherdirectives. Rest tremor is assessed separately for all four limbs andalso for the lip/jaw. Rate only the maximum amplitude that is seen atany time as the final rating.

Extremity Ratings

0: Normal: No tremor. 1: Slight: <1 cm in maximal amplitude. 2: Mild: >1cm but <3 cm in maximal amplitude. 3: Moderate: 3-10 cm in maximalamplitude. 4: Severe: >10 cm in maximal amplitude.

Lip/Jaw Ratings

0: Normal: No tremor. 1: Slight: <1 cm in maximal amplitude. 2: Mild: >1cm but <2 cm in maximal amplitude. 3: Moderate: >2 cm but <3 cm inmaximal amplitude. 4: Severe: >3 cm in maximal amplitude.

3.18 CONSTANCY OF REST TREMOR Instructions to examiner: This itemreceives one rating for all rest tremor and focuses on the constancy ofrest tremor during the examination period when different body parts arevariously at rest. It is rated purposefully at the end of theexamination so that several minutes of information can be coalesced intothe rating.

0: Normal: No tremor. 1: Slight: Tremor at rest is present <25% of theentire examination period. 2: Mild: Tremor at rest is present 26-50% ofthe entire examination period. 3: Moderate: Tremor at rest is present51-75% of the entire examination period. 4: Severe: Tremor at rest ispresent >75% of the entire examination period.

This disclosure provides a facilitated method using improved technologyto expedite and simplify a quantitative evaluation of body movementdisorder and different titration regimens. Enabling multiplequantitative evaluations to be performed by the patient at home may savethe physician time and improve patient outcomes, thus making the processmore efficient. In some examples, at least sixteen of the eighteen Part3 motor tests listed may be suitable for virtual analysis using themethods described herein. Motor tests requiring significant rigidity andpostural stability may require increased physical interaction of theclinician with the patient and are not a good subject for automatedtesting accordingly.

Complex medication regimens, intraday symptom fluctuations and cognitiveissues make managing the disease a challenge for PwP and theircaregivers. Current objective diagnostic sensors and other tools havecost and logistical barriers. They are marketed to healthcareprofessionals and researchers, not PwP who are left to manage theirdisease with inadequate tools.

The technical improvements and functionality disclosed in thisapplication add significant objectivity to standard outcome measuresthat will help advance treatment plans for the hard-to-measure movementdisorders. The evolution and integration of technology and medicinedescribed herein allows movement disorder neurologists, physicians andnurses to have very minimal, if any, interrater variability (IRV) whenusing the UPDRS. The present technology enables pharmaceutical companiesto gather more consistent and accurate data by eliminating thesubjective component in the rating of the UPDRS. This objective standardcan help to show that certain drugs are efficacious across large studypopulations and can potentially save thousands if not millions ofdollars in the process of getting drugs FDA approved in clinical trials.Primary investigators can employ a nurse or research coordinator to dothe majority of the UPDRS assessment with confidence that the ratingwill be the same as if the physician did it.

The present disclosure also significantly decreases the time it takes tocomplete the UPDRS in the research and in clinical practice. Since mostneurologists do not routinely use the UPDRS in clinical practice thepresent application would increase the numbers of those that do. Thepresent disclosure seeks to change the way Parkinson's disease and othermovement disorder patients are evaluated by making this faster and moreaccurate, and by eliminating IRV, human error and subjective scoring.Its application can extend to treatment for diseases such as EssentialTremor, Chorea, Tardive Dyskinesia (TD) and Blepharospasm (BS) will alsoimprove with more precise and objective measures of disease state andrate of change.

Technical solutions of the present disclosure include standardization inthe measurement of abnormal movements. Variability in doctor scores is areality. The present disclosure seeks to provide the same score, for thesame patient (subject), regardless of a doctor or examiner conducting atest. Embodiments of the present disclosure substantially removesubjectivity and human error. The improved technology described hereinfacilitates access to democratized global health care, an objectivemeasurement of symptoms and the effects of medication on symptoms, and amore reliable and accurate view of a disease progression over time.

As a value proposition, the systems and methods described herein allowsymptoms to be conveniently evaluated at home throughout the day. A morecomplete picture of symptoms is provided. While the present systems andmethods may not replace a physician, they save time and money andsubstantially eliminate human error and subjectivity. In some examples,use of the disclosed system is billable to medical aid societies (e.g.Medicare) as part of a neural examination. The time taken to evaluate apatient is minimized, and test data can be visually validated with avideo and photo summary in a test report. In some examples, a nurse mayperform a movement disorder test which qualifies as a neural exam and isbillable to Medicare. A doctor may perform further parts of the test andcopy and paste his or her results into a composite test report.

Global clinical trials using the systems and methods described hereinhave eliminate clinical trial site variability and error rates and allowthe diversified study of different demographic populations globally.

As the inevitable advance in telemedicine proceeds, the present systemsand methods provide access to movement disorder testing globally, forexample in assisting underserved regions. The establishment of globalstandards is facilitated while enabling clinical trial diversity. Theobjectivity of the results adds a validation layer and substantiallyeliminates human error.

Movement disorder diseases that may be treated include chorea, TardiveDyskinesia (TD), Blepharospasm (BS), essential tremor, PD, andDyskinesia. In relation to Dyskinesia specifically, alleviation ofmovement in the arms and neck may alleviate movement in a subject'sface. Some examples employ different observation angles for dyskinesia,for looking down at the top of the head.

Measuring Movement Disorder Symptoms

In some examples, reference objects and computer vision techniques areused to determine the physical scale and the degree of movement ofobjects in the video. Once the scale of the subject, body part or faciallandmark has been determined it is possible to track and measuremovement of the body and its various objects from frame to frame. Forinstance, a subject may be six feet tall and based on that informationan accurate scale of body parts and movement in relation to them can bedeveloped.

Facial Recognition

Some examples employ and object detection tool. An example tool mayinclude an object detection framework for detecting objects in realtime. It is used primarily to detect faces. An example algorithm hasfour stages and works be analyzing pixels within rectangular areas. Allhuman faces share similar properties with respect location of eyes, noseand mouth. In some examples, the algorithm identifies facial regionssuch as the bridge of the nose which has a bright vertical area wherethe bridge of the nose reflects light. With reference to FIGS. 5A-5B,once a face is isolated it can be tracked and measured to determine howquickly the subject stands up and completes testing procedures.

Hand Gesture Recognition

In some examples, an image isolation tool may be used to isolate thehand image from the background of the video based, for example, on skincolor and the contrast from the background. The video background andcertain contours of the hand and fingers may be extracted using anextraction function. Finger tips are convex points and the area inbetween the base of the fingers are defect points. With reference toFIGS. 6A-6B, once the tips of the fingers have been detected it ispossible to track and measure finger tapping speed, amplitude,hesitations, halts and decrementing amplitude objectively.

Facial Landmark Detection

In some examples, video recorded with a single RGB camera is run througha series of algorithms to determine the location of facial features andprovide a scale grid used to measure the movement of the subject. Afeature location tool may be used to locate facial features and plot aseries of markers that map to specific facial regions. With reference toFIG. 7, facial regions of a subject 700 can be detected and may includeregions such as the mouth 702, the right eyebrow 704, the left eyebrow706, the right eye 708, the left eye 710, the nose 712, and the jaw 714.Each region may be defined by a set of points 716.

Measurement Grid

In some examples, computer vision and augmented reality techniques areused to identify and measure objects in a video stream. Once ameasurement scale has been determined, a virtual grid 718 is placed atfront-of-face depth. The virtual grid is used to determine the objectivemeasure of body movement during the video sequence. In some examples,each grid square represents 1 cm of distance laterally at the face.

De-Identification

Patient and study subject identities can be masked and only markersrepresenting the body parts being measured are displayed. This privacyelement has the potential to aid in clinical trial recruitment.

Summary Image Snapshot

Individual body part key points are tracked over the course of the videoand the location of each is stored for analysis. At the end of theprocess an image is created which summarizes the movement of each keypoint with color coded lines. This image is a visual representation ofthe severity of movement and allows for a fast and simple understandingof motor symptoms.

Facial Landmark Overlays

With reference to FIG. 8, facial landmarks, such as one or more of theregions 702-714 shown in FIG. 7, are highlighted visually by connectingthe points 716 in each region to define a respective line 802 for eachregion 702-714. These facial feature outlines 802 are used in someexamples to visually identify and track each facial landmarkindependently.

Tracking Movement

With reference to FIG. 9, in some examples various techniques areemployed to validate the objective measures of movement in relation tothe grid. In some examples, the point of a visual validation is toprovide a quick and intuitive summary of movement that corroboratesother independent data. Summarizing the movement in a manner thatsupports the current measurement by observation will improveunderstanding and adoption of this method. A set 804 of lines 802 foreach facial feature outline may be generated based on the movement ofthe subject 700, for example as shown at 806. With reference to FIG. 10,for privacy or other reasons, the facial landmarks can be separated fromthe background images of the subject 700. This may allow for a morefocused summary of movement in relation to the grid.

Facial Landmark Behavior Detection

With reference to FIGS. 11, 12, and 13, because the facial features1102-1114, 1202-1214, and 1302-1314 can be identified and tracked inrelation to the grid 718, as a group and individually and by themselves,subject behaviors such as opening a mouth, raising an eyebrow, tilting ahead and closing an eye can be detected and measured with objectiveprecision. Some examples can include determining how many times thesubject blinked or closed an eye, or how many milliseconds was an eyeclosed. Other examples may determine whether the duration and severityof the closed eye increased over time. These aspects and others can bemeasured and reported objectively thereby saving time and improvinginsight for scarce medical specialists, measuring the impact ofmedications objectively and without friction and improving outcomes forpatients who are empowered to understand their condition by measuringtheir own symptoms.

Measuring Facial Landmark Amplitude

With reference to FIG. 14, in some examples the coordinates of facialfeatures such as eyes, nose, mouth, eyebrows, top of left jawline andtop of right jawline are measured for vertical, horizontal amplitude andvelocity of movement. The features are labeled in the view as noted inthe table below.

Region Region Code Vertical Horizontal Right Eye B (1408) 2.8 cm 8.5 cmLeft Eye C (1410) 2.3 cm 4.1 cm Nose E (1412) 2.8 cm 8.1 cm Mouth F(1402) 2.0 cm 5.4 cm Right Jawline A (1414) 3.0 cm 5.0 cm Left Jawline D(1416) 2.7 cm 5.9 cm Right Eyebrow G (1404) 2.4 cm 8.4 cm Left Eyebrow H(1406) 2.2 cm 4.2 cm

This movement information is directly relevant for measuring the effectof medications on movement disorders such as dyskinesias where symptomsmay fluctuate intra-day and the subject may not even realize they areexperiencing movement at all.

Measuring Body Part Amplitude

In some examples, with reference to FIGS. 15 and 16, the coordinates ofthe head, shoulders, chest, waist, elbows and hands of a subject 700 aremeasured for vertical, horizontal amplitude and velocity of movement.Example directions of reciprocating or shaky head, elbow and handmovements are indicated at 1520, 1522, and 1524, respectively.

Region Region Code Vertical Horizontal Head A (1502) 1.3 cm 6.2 cm RightShoulder B (1504) 1.9 cm 1.4 cm Left Shoulder C (1506) 2.2 cm 1.8 cmChest D (1508) 0.2 cm 1.4 cm Right Elbow E (1510) 2.7 cm 5.5 cm LeftElbow F (1512) 0.3 cm 1.2 cm Right Hand H (1516) 2.4 cm 8.4 cm Left HandI (1518) 5.8 cm 5.4 cm

In further examples of measuring movement disorder symptoms, deeplearning pose estimation algorithms are used for vision-based assessmentof parkinsonism and levodopa-induced dyskinesia (LID). A deep learningpose estimation method may be used to extract movement trajectories fromvideos of PD assessments. Features of the movement trajectories can beused to detect and estimate the severity of parkinsonism.

FIGS. 17A-17D represent operations in an example image processing of avideo to measure and visualize the level of a subject's dyskinesia. FIG.17A depicts an image of a subject (patient) undergoing a PD assessment.In FIG. 17B, a “skeleton extraction” operation is performed. A completeskeleton of the patient may be extracted. In some examples, a skeletonis extracted using extraction tool. In some examples, the extractiontool is a real-time multi-person system to jointly detect human body,hand, and facial key points (in total 135 key points) on single images.In some examples, the 135 key points comprise the following: 15, 18, or25-key point body or foot estimations; 2 times 21-key point hand keypoint estimation (two hands), and 70-key point face key pointestimation. Dyskinesial movement of a skeleton which is representativeof Dyskinesial movement of the patient may be observed objectively(without intervention of a human observer) using the extraction toolwith reference to a grid 718. Example results of the assessment may begraphed as shown in FIGS. 17C-17D.

FIG. 18 depicts an example face pose output format 1800 for measuredface movement with key points (features) numbered as shown. FIG. 19depicts an example body pose output format 1800 for body (skeleton 1700)movement assessment with key points (features) numbered as shown. Theposition of each numbered key point is recorded at each frame and isused to make the aggregate facial or body movement measurements. Thedisplacement and velocity of the key points are used to measure themovement of prospective body parts including for example the neck, thefingers, the hands and legs, and so forth, as depicted in FIGS. 17C-17D,for example.

In some examples, a visualization of face or body movement is performed.Such measurement it typically recorded in pixels. Movement in pixelsdoes not provide a real-world analysis. A movement expressed in pixelsis converted to real world units, such as centimeters (cm) for example.In some examples, a conversion to real world scale requires theestimation of a relative scale between pixels and centimeters. Anestimation of scale involves various challenges such as a depthestimation which is not possible with mobile phone camera as ittypically only includes a single monocular camera which is uncalibrated.Calibration parameters cannot be determined in real-time as each phonehas a different camera and a generic calibration across devices is notpossible.

To address the issue, in some examples a linear approach is applied. Theheight in cm and the weight of the patient are known. An image of thepatient is taken in which he is standing straight to imitate actualheight. This frame is called the reference frame. The height of thepatient is mapped to the pixel distance between head to toe to get arelative scale (r). With this relative scale (r), some actual horizontaland vertical distances between key points are stored. Example distanceswhich may be stored are: a distance between the two shoulders, a facewidth, a height of torso, a length of arm, a length of leg from knee totop and a length of the leg from knee to toe.

However, on occasion it may not be possible to use only one distance asa reference dimension as the associated particular body part maydisappear from the frame as the person (or part) moves closer to thecamera. Moreover, when a patient is standing perpendicular to the camerafacing left or right, the visible horizontal distances may diminish downto a zero value and hence cannot be used as reference. Similarly, if theobserved patient bends down, the vertical distances cannot be used asreferences and hence horizontal distances are determined in someexamples to calculate the scale at that point. Thus, the human bodyitself may be used as a reference to measure the scale of movement ateach of several points in a video, thus obviating the need for anindependent reference object.

The may provide certain advantages. For example, a reference objectcannot be used here for the reason that as the patient moves closer oraway from the camera, the reference object cannot be moved along withthe patient. The patient is self-calibrating, as it were. Thus, one ormore body parts may be used to determine a reference scale.

In some examples, a reference distance (D) and the distance betweenpixels (d) in a current frame and the distance between the correspondingpixels in a reference frame (d_(ref)) are used to calculate a relativescale (r) between a pixel and a real-world scaling system. This relativescale can be used to calculate the movement in the current frame, where:

$r = {\frac{d}{d_{ref}}{XD}}$

This relative scale is used to measure the movement of pixels in cm. Ameasured movement expressed in pixels is multiplied by (i.e. scaled upor scaled down) the relative scale (r) to derive a real-world face ormeasurement. In some examples, the calculated movement in centimeters isused to infer the level of dyskinesia of the patient. With reference toFIGS. 20A-20D, in some examples, a reference scale is used inassociation with the overlay of a grid 718 in images of a video toapproximate measurement of a subject 2000 in a real-world scale. Eachbox of the grid 718 may be a square of side 10 cm, for example. As thesubject 2000 moves toward or away from the camera, the reference scalechanges. As the scale changes dynamically, so the grid is changeddynamically, accordingly.

The images in FIGS. 20A-20D represent different poses of a subject 2000alongside extracted skeletons 1700 displayed within a measurement orreference grid overlaid images in the video and sized according to thereference scale. These depictions may assist in providing a visualunderstanding of the degree of movement of a subject in real-worldscale.

In some examples, distances between key points are used as a referenceto measure a scale at each reference point. In such cases, anotherreference object cannot be used as a reference as the object cannot movealong with the patient and the scale changes as the patient movestowards or away from the camera. Here, a distance between adjacent keypoints may be used as reference instead. In some examples, a measurementsystem measures original distances between adjacent keyframes in a firstvideo frame with the height of the patient standing and a relative scaleis determined according to changes in these original distances. Atrajectory of the key points may be plotted to determine a degree ofrandomness of the assessed facial or body movement.

For example, and with reference to FIGS. 21A-21D, example movementtrajectories 2100 of assessed subjects may be displayed, each subjecthaving a different level of dyskinesia in the views. It may also beobserved from the images in FIGS. 21A-21D that the randomness in thesubject's movement increases as the level of dyskinesia increases. Thisvisualization can help doctors to determine the level of dyskinesia ofthe subject.

In some examples, measurement of a level of dyskinesia may depend on anumber of factors, for example a measurement of the movementdisplacement, and a measurement of the movement frequency. In order tomeasure an amplitude of displacement of movement, the movement of eachof the key points in both directions, i.e. x and y directions, istracked. In a particular direction of movement (i.e. x or y), a keypoint can move in one of two ways i.e. left or right in the x-direction,or up or down in the y-direction. One of the direction is taken as apositive and other direction is taken as negative, and the displacement(amplitude) is measured with respect to a position in the previoussecond. A net displacement per second is calculated and plotted versustime on a graph.

FIGS. 22A-22D depict example graphs 2200 with results plotted for x andy displacement of an example neck key point illustrating various levelsof dyskinesia. The displacement or amplitude of the associated level ofdyskinesia increases in the views from FIG. 22A to FIG. 22D. Thepositive and negative y-axes represent movement in two oppositedirections with amplitudes as shown. The various graphs representdisplacement on an x-axis and a y-axis. It may be observed from thegraphs that maximum displacement increases as the level of dyskinesiaincreases. This may serve to justify the validity of using displacementas a metric for as assessed degree of dyskinesia.

Turning now to measurement of frequency, in some examples an amplitudeof movement at a key point is determined or fixed, and with respect towhich a frequency of movement at the key point is established. In someexamples, a number of cycles of movement that a key point makes betweenpositive and negative values of the fixed amplitude is measured. In someexamples, in order to keep an amplitude fixed, a range of displacementover a complete dataset is analyzed to derive an average amplitude forall levels of dyskinesia. To differentiate between levels of dyskinesiaand to provide a metric which may work as a good estimator, a value ofAmplitude (A) of a given key point is fixed and a number of cycles thekey point makes between −A and +A is determined.

FIGS. 23A-23D depict example graphs 2300 with results representingmeasured frequencies corresponding to different levels of dyskinesia.The graphs represent the frequency of movement of a neck key pointbetween amplitudes of movement +A and −A. An amplitude A of 25 pixelswas used in the illustrated case. It may be observed from the graphs2300 that the frequency of movement increases with the level ofdyskinesia. This may also serve to show that frequency is a helpfulmetric to establish levels of dyskinesia.

In some examples, the intervention of a human observer is not requiredto determine a level of dyskinesia. Some examples provide a fullyautomatic system to determine a level of dyskinesia. Some examplesemploy a machine learning model which extracts information frompre-processed videos and predicts a level of dyskinesia for a givensubject.

An example machine learning model is now described. A set of video clipsof subjects having differing levels of dyskinesia was assembled andcategorized from 0-4 (no dyskinesia to very serious dyskinesia). In someexamples, the frames per second (FPS) value of the video clips in eachset were not consistent and required normalization before being placedinto a machine learning model.

In some examples, an FPS of 15 was determined to be a relativelybalanced value for machine learning purposes. The value was efficient inneeding lower levels of computational power only, while remaining stableand informative as the same time.

Some examples included video clip duration normalization. Here,different duration values were assessed for machine learning and it wasdetermined that video clips of 20 seconds in duration carried sufficientmovement information for prediction purposes, and so in some examplesthe final 20 seconds of full video clips were used as preprocessed,normalized training samples.

In some examples, a data generation operation is performed. Here, anextraction tool as described above may be used to detect a patient's keypoint movement during a test. The movement of all key points of interestper second may be stored into a file. A video of the movement of eachkey point may have has a specific folder storing all data files for thatpoint. Some examples include a software program for organizing all datafolders into a certain format for example by shape and a certain order,and for placing the folders into a file specifically prepared for amachine learning model. Some examples utilize Gaussian normalization toprocess the training data.

Some examples allow prediction by a machine learning technique. Here,long short term memory (LSTM) may be units of a recurrent neural network(RNN). An RNN composed of LSTM units is typically referred to as an LSTMnetwork. An LSTM unit is composed of a cell, an input gate, an outputgate and a forget gate. The cell remembers values over arbitrary timeintervals and the three gates regulate the flow of information into andout of the cell.

In example training operations, preprocessed data files as discussedabove for example are placed into an LSTM model. After a period oftraining, for example two days, the model may reach approximately 100%accuracy for the collected set of video clips. In some examples, testingis created for predicting measurement results in videos in the realworld. If the predicted results do not match actual scores derived for agiven patient, this may indicate that further training for a machinemodel is required for higher accuracy.

Example operations in a machine learning process may include those shownin FIGS. 24-25. For example, operations in a machine training process2400 (FIG. 24) may include: at 2402, video FPS normalization; at 2404,video clip duration normalization; at 2406, data generation, at 2408,data preprocessing, and at 2410, machine training. Example operations ina process 2500 (FIG. 25) for testing a trained model may include: at2502, video FPS normalization; at 2504, video duration normalization; at2506, data generation; at 2508, data preprocessing; and, at 2510, modeltesting.

An example architecture 2600 of a training program may include thecomponents shown in FIG. 26. An example architecture 2700 for componentsin a prediction process are shown in FIG. 27.

With reference to FIG. 28, an example system of the present disclosurecombines exercise with mobile software on a smart device 2800 (forexample, an application such as pdFIT) to provide coaching andbiofeedback 2802 in order to improve Parkinson's disease symptoms anddelay disease progression. A two-year study showed that subjects who usepdFIT regularly had statistically-significantly improvement in theirmotor control over the entire study period.

Medication/Symptom Tracking

With reference to FIG. 29, medication reminders may help subjects becomemore adherent to their treatment plans which improves outcomes. Bylayering objective outcome measures (such as the results of a series oftap tests 2902 conducted via the screen of a smart device 2900 forexample), over a subject's medication dosage or timing regimen, asubject can be made increasingly aware of how movement disorder symptomschange hour-to-hour and over time. Sharing this information with aphysician may result in a more informed treatment plan.

Before and after Intraday Medication/Symptom Charts

One of the challenges for a subject managing Parkinson's disease isunderstanding the timing and severity of their symptoms and how theyrespond to medications. For example, Levodopa is a medication that has apositive effect on motor symptoms but becomes less effective over timeand usually causes additional symptoms such as dyskinesia and psychosis.Off episodes or off-time are the periods of a day when Levodopa'seffectiveness wanes and symptoms such as bradykinesia and tremor makefunctioning difficult. With reference to FIGS. 30A-30B, the charts 3000and 3002 illustrate how understanding motor control symptoms andmedication dose/timing during a day can influence treatment plans. Theundulating lines 3004 and 3006 in each chart represent a subject'sfinger tapping test scores, for example.

In a before-medication chart 3000, the line 3004 is representative ofmajor fluctuations in the subject's motor control. The troughs of thered line 3004 represent subject off time. This may indicate or be causedby the subject taking three doses of Levodopa per day, for example. Inthe after-medication chart 3002, the example treatment plan was changedto four doses a day of extended release Levodopa. The flattening of themotor control line 1306 represents an elimination of patient off-time.

Symptom/Medication Longitudinal Chart

It may be important for patients and care givers to understand howmedication is affecting symptoms and how the disease is progressing.Armed with longitudinal medication and symptom data, a PwP is empoweredto influence their treatment plans. In addition to being more engaged intheir situation through symptom tracking, the tools of the presentdisclosure help to improve outcomes by helping subjects be more adherentto their medication regimen.

With reference to FIG. 31, the illustrated chart 3100 indicates resultsof movement disorder testing in an example embodiment. Here, a subject'smotor control is getting worse during the first two weeks of the testperiod as depicted by the first trend line 3102, for example. Thesubject was then placed on Levodopa medication. This change in treatmentplan resulted in an immediate and dramatic improvement in motor controlas seen by the individual finger tapping scores immediately followingthe introduction of Levodopa. This is depicted by the second trend line3104. The trend line 3104 extends out a couple months and denotes asustained and gradual improvement in motor control.

Symptom Complexity

Parkinson's disease is also known as a “snowflake disease” because eachinstance is unique to the individual. In addition to motor controlissues such as tremors rigidity, bradykinesia, postural instability,gait issues and vocal issues, PwP are also affected with non-motorsymptoms such as cognitive changes, sleep disorders, depression,anxiety, hallucinations and delusions, fatigue, hypotension, sexualconcerns and vision issues.

The most effective PD medication is Levodopa which is converted todopamine in the brain. Unfortunately, Levodopa is directly responsiblefor the introduction of additional symptoms that require management.These symptoms include dyskinesia, psychosis and impulse-controldisorder (binge eating, excessive shopping and gambling,hypersexuality). Many newly-approved and Phase 3 medications addressLevodopa-induced side effects.

Levodopa and Dyskinesia

Early-stage patients are more easily managed on Levodopa. Levodoparemains as the most effective treatment for PD, and over 75% of thepatients with PD receive Levodopa. However, long term treatment withLevodopa leads to seriously debilitating motor fluctuations, i.e. phasesof normal functioning (ON-time) and decreased functioning (OFF-time).

Furthermore, as a result of the use of high doses of Levodopa withincreasing severity of the disease, many patients experience involuntarymovements known as Levodopa-Induced Dyskinesia (LID). As the diseaseprogresses, more drugs are used as an add-on to what the patient alreadytakes, and the focus is to treat symptoms while managing LID and the“off-time” effects of Levodopa.

Most current therapies target the dopaminergic system that is implicatedin the pathogenesis of PD, and most current treatments act by increasingdopaminergic transmission that leads to amelioration of motor symptoms.In addition to being more engaged in their situation through symptomtracking, the tools of the present disclosure improve outcomes byhelping subjects be more adherent to their medication regimen.

Thus, in some embodiments, there is provided a system for measuring bodymovement in movement disorder disease, the system comprising: at leastone processor and a memory storing processor executable codes, which,when implemented by the at least one processor, cause the system toperform operations comprising, at least: receiving a video including asequence of images; detecting at least one object of interest in one ormore of the images; locating feature reference points of the at leastone object of interest; generating a virtual movement-detectionframework in one or more of the images; detecting, over the sequence ofimages, at least one singular or reciprocating movement of the featurereference point relative to the virtual movement-detection framework;and generating a virtual path tracking a path of the at least onedetected singular or reciprocating movement of the feature referencepoint.

In some embodiments, the operations may further comprise positioning oraligning the virtual movement-detection framework with the at least oneobject of interest in one or more of the images based at least in parton a feature reference point. In some embodiments, the operationsfurther comprise analyzing coordinates of the virtual path or featurereference point to derive an amplitude of the at least one singular orreciprocating movement of the feature reference point.

In some embodiments, the operations further comprise analyzingcoordinates of the virtual path or feature reference point to derive afrequency of the at least one singular or reciprocating movement of thefeature reference point. In some embodiments, the operations furthercomprise associating the detected at least one singular or reciprocatingmovement, or the virtual path, with a body movement disorder selectedfrom a plurality of body movement disorders. In some embodiments, theoperations further comprise generating or presenting a disorder statusof an individual based on the associated body movement disorder selectedfrom the plurality of body movement disorders.

In some embodiments, the operations further comprise transmitting acommunication including data associated with the disorder status basedon or including a trend in the disorder status.

Some embodiments of the present disclosure include method embodiments.With reference to FIG. 32, a method 3200 may comprise: at 3202,receiving a video including a sequence of images; at 3204, detecting atleast one object of interest in one or more of the images; at 3206,locating feature reference points of the at least one object ofinterest; at 3208, generating a virtual movement-detection framework inone or more of the images; at 3210, detecting, over the sequence ofimages, at least one singular or reciprocating movement of the featurereference point relative to the virtual movement-detection framework;and, at 3212, generating a virtual path tracking a path of the at leastone detected singular or reciprocating movement of the feature referencepoint.

The method 3200 may further comprise positioning or aligning the virtualmovement-detection framework with the at least one object of interest inone or more of the images based at least in part on a feature referencepoint.

The method 3200 may further comprise analyzing coordinates of thevirtual path or feature reference point to derive an amplitude of the atleast one singular or reciprocating movement of the feature referencepoint.

The method 3200 may further comprise analyzing coordinates of thevirtual path or feature reference point to derive a frequency of the atleast one singular or reciprocating movement of the feature referencepoint.

The method 3200 may further comprise associating the detected at leastone singular or reciprocating movement, or the virtual path, with a bodymovement disorder selected from a plurality of body movement disorders.

The method 3200 may further comprise generating or presenting a disorderstatus of an individual based on the associated body movement disorderselected from the plurality of body movement disorders.

The method 3200 may further comprise transmitting a communicationincluding data associated with the disorder status based on or includinga trend in the disorder status.

Example embodiments also include machine-readable media includinginstructions which, when read by a machine, cause the machine to performoperations comprising at least those summarized above, or describedelsewhere herein.

Although the subject matter has been described with reference tospecific example embodiments, it will be evident that variousmodifications and changes may be made to these embodiments withoutdeparting from the broader spirit and scope of the disclosed subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof, show by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be utilized and derivedtherefrom, such that structural and logical substitutions and changesmay be made without departing from the scope of this disclosure. ThisDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by any appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

What is claimed is:
 1. A system for measuring body movement in movementdisorder disease, the system comprising: at least one processor and amemory storing processor executable codes, which, when implemented bythe at least one processor, cause the system to perform operationscomprising, at least: receiving a video including a sequence of images;detecting at least one object of interest in one or more of the images;locating feature reference points of the at least one object ofinterest; generating a virtual movement-detection framework in one ormore of the images; detecting, over the sequence of images, at least onesingular or reciprocating movement of the feature reference pointrelative to the virtual movement-detection framework; and generating avirtual path tracking a path of the at least one detected singular orreciprocating movement of the feature reference point.
 2. The system ofclaim 1, wherein the operations further comprise: positioning oraligning the virtual movement-detection framework with the at least oneobject of interest in one or more of the images based at least in parton a feature reference point.
 3. The system of claim 1, wherein theoperations further comprise: analyzing coordinates of the virtual pathor feature reference point to derive an amplitude of the at least onesingular or reciprocating movement of the feature reference point. 4.The system of claim 1, wherein the operations further comprise:analyzing coordinates of the virtual path or feature reference point toderive a frequency of the at least one singular or reciprocatingmovement of the feature reference point.
 5. The system of claim 1,wherein the operations further comprise: associating the detected atleast one singular or reciprocating movement, or the virtual path, witha body movement disorder selected from a plurality of body movementdisorders.
 6. The system of claim 5, wherein the operations furthercomprise: generating or presenting a disorder status of an individualbased on the associated body movement disorder selected from theplurality of body movement disorders.
 7. The system of claim 6, whereinthe operations further comprise: transmitting a communication includingdata associated with the disorder status based on or including a trendin the disorder status.
 8. A method comprising: receiving a videoincluding a sequence of images; detecting at least one object ofinterest in one or more of the images; locating feature reference pointsof the at least one object of interest; generating a virtualmovement-detection framework in one or more of the images; detecting,over the sequence of images, at least one singular or reciprocatingmovement of the feature reference point relative to the virtualmovement-detection framework; and generating a virtual path tracking apath of the at least one detected singular or reciprocating movement ofthe feature reference point.
 9. The method of claim 8, furthercomprising: positioning or aligning the virtual movement-detectionframework with the at least one object of interest in one or more of theimages based at least in part on a feature reference point.
 10. Themethod of claim 8, further comprising: analyzing coordinates of thevirtual path or feature reference point to derive an amplitude of the atleast one singular or reciprocating movement of the feature referencepoint.
 11. The method of claim 8, further comprising: analyzingcoordinates of the virtual path or feature reference point to derive afrequency of the at least one singular or reciprocating movement of thefeature reference point.
 12. The method of claim 8, further comprising:associating the detected at least one singular or reciprocatingmovement, or the virtual path, with a body movement disorder selectedfrom a plurality of body movement disorders.
 13. The method of claim 12,further comprising: generating or presenting a disorder status of anindividual based on the associated body movement disorder selected fromthe plurality of body movement disorders.
 14. The method of claim 13,further comprising: transmitting a communication including dataassociated with the disorder status based on or including a trend in thedisorder status.
 15. A machine-readable medium including instructionswhich, when read by a machine, cause the machine to perform operationsincluding, at least: receiving a video including a sequence of images;detecting at least one object of interest in one or more of the images;locating feature reference points of the at least one object ofinterest; generating a virtual movement-detection framework in one ormore of the images; detecting, over the sequence of images, at least onesingular or reciprocating movement of the feature reference pointrelative to the virtual movement-detection framework; and generating avirtual path tracking a path of the at least one detected singular orreciprocating movement of the feature reference point.
 16. The medium ofclaim 15, wherein the operations further comprise: positioning oraligning the virtual movement-detection framework with the at least oneobject of interest in one or more of the images based at least in parton a feature reference point.
 17. The medium of claim 15, wherein theoperations further comprise: analyzing coordinates of the virtual pathor feature reference point to derive an amplitude of the at least onesingular or reciprocating movement of the feature reference point. 18.The medium of claim 15, wherein the operations further comprise:analyzing coordinates of the virtual path or feature reference point toderive a frequency of the at least one singular or reciprocatingmovement of the feature reference point.
 19. The medium of claim 15,wherein the operations further comprise: associating the detected atleast one singular or reciprocating movement, or the virtual path, witha body movement disorder selected from a plurality of body movementdisorders.
 20. The medium of claim 19, wherein the operations furthercomprise: generating or presenting a disorder status of an individualbased on the associated body movement disorder selected from theplurality of body movement disorders.